




import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
snake_case_  :      Union[str, Any]		=		logging.getLogger(__name__)



def        __a							(  ) ->					Any:
							"""simple docstring"""




							lowerCamelCase_			:    Any   						=      argparse.ArgumentParser(
							    description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."      )
							parser.add_argument("--file_path"   ,		type=UpperCAmelCase_   ,		default="data/dump.txt"   ,		help="The path to the data."      )
							parser.add_argument("--tokenizer_type"   ,		type=UpperCAmelCase_   ,		default="bert"   ,		choices=["bert", "roberta", "gpt2"]      )
							parser.add_argument("--tokenizer_name"   ,		type=UpperCAmelCase_   ,		default="bert-base-uncased"   ,		help="The tokenizer to use."      )
							parser.add_argument("--dump_file"   ,		type=UpperCAmelCase_   ,		default="data/dump"   ,		help="The dump file prefix."      )
							lowerCamelCase_			:    Tuple   						=      parser.parse_args()

							logger.info(f"Loading Tokenizer ({args.tokenizer_name})"      )
							if args.tokenizer_type == "bert":
														lowerCamelCase_			:    int   						=      BertTokenizer.from_pretrained(args.tokenizer_name      )
														lowerCamelCase_			:    Any   						=      tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
														lowerCamelCase_			:    int   						=      tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
							elif args.tokenizer_type == "roberta":
														lowerCamelCase_			:    int   						=      RobertaTokenizer.from_pretrained(args.tokenizer_name      )
														lowerCamelCase_			:    Union[str, Any]   						=      tokenizer.special_tokens_map["cls_token"]  # `<s>`
														lowerCamelCase_			:    Tuple   						=      tokenizer.special_tokens_map["sep_token"]  # `</s>`
							elif args.tokenizer_type == "gpt2":
														lowerCamelCase_			:    int   						=      GPTaTokenizer.from_pretrained(args.tokenizer_name      )
														lowerCamelCase_			:    Union[str, Any]   						=      tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
														lowerCamelCase_			:    str   						=      tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

							logger.info(f"Loading text from {args.file_path}"      )
							with open(args.file_path   ,		"r"   ,		encoding="utf8"      ) as fp:
														lowerCamelCase_			:    Any   						=      fp.readlines()

							logger.info("Start encoding"      )
							logger.info(f"{len(UpperCAmelCase_      )} examples to process."      )

							lowerCamelCase_			:    Tuple   						=      []
							lowerCamelCase_			:    Optional[Any]   						=      0
							lowerCamelCase_			:    Any   						=      10000
							lowerCamelCase_			:    Union[str, Any]   						=      time.time()
							for text in data:
														lowerCamelCase_			:    Tuple   						=      f"{bos} {text.strip()} {sep}"
														lowerCamelCase_			:    List[str]   						=      tokenizer.encode(UpperCAmelCase_   ,		add_special_tokens=UpperCAmelCase_      )
														rslt.append(UpperCAmelCase_      )

														iter += 1
														if iter % interval == 0:
																					lowerCamelCase_			:    Any   						=      time.time()
																					logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"      )
																					lowerCamelCase_			:    Dict   						=      time.time()
							logger.info("Finished binarization"      )
							logger.info(f"{len(UpperCAmelCase_      )} examples processed."      )

							lowerCamelCase_			:    Dict   						=      f"{args.dump_file}.{args.tokenizer_name}.pickle"
							lowerCamelCase_			:    int   						=      tokenizer.vocab_size
							if vocab_size < (1 << 16):
														lowerCamelCase_			:    Dict   						=      [np.uintaa(UpperCAmelCase_      ) for d in rslt]
							else:
														lowerCamelCase_			:    Optional[int]   						=      [np.intaa(UpperCAmelCase_      ) for d in rslt]
							random.shuffle(rslt_      )
							logger.info(f"Dump to {dp_file}"      )
							with open(UpperCAmelCase_   ,		"wb"      ) as handle:
														pickle.dump(rslt_   ,		UpperCAmelCase_   ,		protocol=pickle.HIGHEST_PROTOCOL      )


if __name__ == "__main__":
	main()



"""simple docstring"""


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				float							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				bool = False							,   )->	str:







              """simple docstring"""
              super().__init__()

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		False

              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )

              UpperCamelCase       			=		TaConfig(
                  vocab_size=UpperCAmelCase_							,   d_model=UpperCAmelCase_							,   num_heads=UpperCAmelCase_							,   d_kv=UpperCAmelCase_							,   d_ff=UpperCAmelCase_							,   dropout_rate=UpperCAmelCase_							,   feed_forward_proj=UpperCAmelCase_							,   is_decoder=UpperCAmelCase_							,   is_encoder_decoder=UpperCAmelCase_							,   )

              UpperCamelCase       			=		nn.ModuleList()
              for lyr_num in range(UpperCAmelCase_      ):
                     UpperCamelCase       			=		TaBlock(UpperCAmelCase_      )
                     self.encoders.append(UpperCAmelCase_      )

              UpperCamelCase       			=		TaLayerNorm(UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.token_embedder(UpperCAmelCase_      )

              UpperCamelCase       			=		encoder_input_tokens.shape[1]
              UpperCamelCase       			=		torch.arange(UpperCAmelCase_							,   device=encoder_input_tokens.device      )
              x += self.position_encoding(UpperCAmelCase_      )

              UpperCamelCase       			=		self.dropout_pre(UpperCAmelCase_      )

              # inverted the attention mask
              UpperCamelCase       			=		encoder_input_tokens.size()
              UpperCamelCase       			=		self.get_extended_attention_mask(UpperCAmelCase_							,   UpperCAmelCase_      )

              for lyr in self.encoders:
                     UpperCamelCase       			=		lyr(UpperCAmelCase_							,   UpperCAmelCase_      )[0]
              UpperCamelCase       			=		self.layer_norm(UpperCAmelCase_      )

              return self.dropout_post(UpperCAmelCase_      ), encoder_inputs_mask





